Abstract: A fluid is modeled as a set of discrete particles. Each of the particles is associated with one or more properties, and a spatial distance comprising a smoothing length over which the one or more properties are to be smoothed. A corresponding trajectory is simulated for each of the particles. The corresponding trajectory is used to formulate a first solution for simulating transport within the fluid. A first predicted error is determined for the first solution. An iterative adjustment is performed to at least one of: a quantity of particles, the smoothing length, or the one or more corresponding properties, to formulate a second solution for simulating transport with the fluid, and a second predicted error is determined for the second solution, until the second predicted error is within a predefined boundary.

Abstract: A method for data mining on compressed data vectors by a certain metric being expressible as a function of the Euclidean distance is suggested. In a first step, for each compressed data vector, positions and values of such coefficients having the largest energy in the compressed data vector are stored. In a second step, for each compressed data vector, the coefficients having not the largest energy in the compressed data vector are discarded. In a third step, for each compressed data vector, a compression error is determined in dependence on the discarded coefficients in the compressed data vector. In a fourth step, at least one of an upper and a lower bound for the certain metric is retrieved in dependence on the stored positions and the stored values of the coefficients having the largest energy and the determined compression errors.

Abstract: A sensor data fusion system includes a processor coupled to a plurality of sensors. The system is initialized by providing access to a data store storing at least one time series of sensor data; a semantic store storing semantic data including system variables, and relations between the system variables; and a mapping therebetween. A registration of a set of one or more variables of interest for which appropriate data is not available is obtained. An initially empty inference model is extended with the set of variables, to obtain an extended model. A request to observe a given one of the set of variables at a given timestamp is obtained. Responsive thereto, time series data for the set of registered variables is retrieved. The extended model is run with the retrieved data to obtain an estimate of the given one of the variables at the given timestamp.

Abstract: Embodiments for automated data exploration and validation by a processor. One or more optimal data flows are provided in response to a query for one or more heterogeneous data sources according to an inference model based on a knowledge graph a plurality of data flows between one or more heterogeneous data sources relating to the query. An analytical flow is provided for one or more of the plurality of data flows for those of the one or more heterogeneous data sources that are undetected, and two or more of the one or more of the plurality of data flows are aggregated or disaggregated for the one or more heterogeneous data sources that are nested within the knowledge graph. One or more criteria is received from a user via an interactive graphical user interface (GUI) to use for defining the one or more optimal data flows.

Abstract: Embodiments for groundwater and solar energy usage optimization for an agricultural region in an Internet of Things (IoT) computing environment by one or more processors are described. An amount of water required for an agricultural region and an amount of solar energy required to pump the water in a water pumping system for the agricultural region may be determined according to groundwater characteristics, weather data, weather forecasts, solar energy forecasts, historical water pumping data, crop and soil characteristics, agricultural management strategies, or a combination thereof.

Abstract: Various embodiments are provided for cognitive data curation in an Internet of Things (IoT) computing environment by a processor. Each data flow and mapping of the data flows may be related to one or more concepts and relationships between the one or more concepts. One or more inconsistencies may be identified between those data flows used to answer a query for time-series data pertaining to the one or more concepts. The inconsistencies between those of the plurality of data flows may be corrected using inference and reasoning via a machine learning operation.

Abstract: Embodiments for automated data exploration and validation by a processor. One or more optimal data flows are provided in response to a query for one or more heterogeneous data sources according to an inference model based on a knowledge graph of heterogeneous data source relationships, a plurality of data flows between one or more heterogeneous data sources relating to the query, and an ontology of concepts and representing a domain knowledge of the one or more heterogeneous data sources.

Abstract: In one aspect, a method for reducing uncertainty in a hydraulic model of a water distribution network due to uncertain parameters and faults in the water distribution network is provided which includes the steps of: (i) calculating an optimized placement of sensors throughout a given uncertain section of the water distribution network; (ii) collecting data from the sensors; (iii) partitioning the given uncertain section of the water distribution network into observable and unobservable sub-sections based on the hydraulic model and a) a position, b) a number, and/or c) a type of the sensors that are available; (iv) correcting uncertain parameters and identifying faults for each of the observable sub-sections; (v) calculating a global uncertainty value for each of the unobservable sub-sections; and (vi) repeating the steps (i)-(vi) iteratively, at each iteration selecting an uncertain sub-section of the water distribution network, until no uncertain sub-sections of the water distribution network remain.

Abstract: In an approach for adaptive sampling of smart meter data, a computer retrieves one or more balancing constraints associated with one or more smart meter sensors. The computer retrieves meter sensor data from the one or more smart meter sensors according to the one or more balancing constraints. The computer determines a subsample of the meter sensor data based, at least in part, on one or more similar consumption patterns of meter sensor data, and then transmits the subsample of the meter sensor data to an optimization engine for use in solving an optimization problem.

Abstract: A system for posterior estimation of variables. Receiving a set of data inputs. Determining a first model of the water distribution network based on the set of data inputs. Determining a second model of the water distribution network based on the set of data inputs, and the first model.

Abstract: A method includes loading a guest virtual machine onto a host system, determining, with the host system, an encapsulation method to be used in association with a virtual network associated with the guest virtual machine, determining an overhead value based on the encapsulation method, determining an adjusted maximum transmission unit (MTU) value based on the overhead value, and passing, information related to the adjusted MTU value from the host system to the guest virtual machine.

Abstract: A system for posterior estimation of variables. Receiving a set of data inputs. Determining a first model of the water distribution network based on the set of data inputs. Determining a second model of the water distribution network based on the set of data inputs, and the first model.

Abstract: A fluid is modeled as a set of discrete particles. Each of the particles is associated with one or more properties, and a spatial distance comprising a smoothing length over which the one or more properties are to be smoothed. A corresponding trajectory is simulated for each of the particles. The corresponding trajectory is used to formulate a first solution for simulating transport within the fluid. A first predicted error is determined for the first solution. An iterative adjustment is performed to at least one of: a quantity of particles, the smoothing length, or the one or more corresponding properties, to formulate a second solution for simulating transport with the fluid, and a second predicted error is determined for the second solution, until the second predicted error is within a predefined boundary.

Abstract: Embodiments for providing aquatic epidemic alerts by a processor are described. Information associated with an aquatic epidemic in a body of water is received. A threat level associated with the aquatic epidemic for a location within the body of water is determined based on the information associated with the aquatic epidemic and water flow data associated with the body of water. An indication of the threat level is generated.

Abstract: Embodiments for automated data exploration and validation by a processor. One or more optimal data flows are provided in response to a query for one or more heterogeneous data sources according to an inference model based on a knowledge graph of heterogeneous data source relationships, a plurality of data flows between one or more heterogeneous data sources relating to the query, and an ontology of concepts and representing a domain knowledge of the one or more heterogeneous data sources.

Abstract: In an approach for adaptive sampling of smart meter data, a computer retrieves one or more balancing constraints associated with one or more smart meter sensors. The computer retrieves meter sensor data from the one or more smart meter sensors according to the one or more balancing constraints. The computer determines a subsample of the meter sensor data, and then transmits the subsample of the meter sensor data to an optimization engine for use in solving an optimization problem.

Abstract: Embodiments for providing aquatic epidemic alerts by a processor are described. Information associated with an aquatic epidemic in a body of water is received. A threat level associated with the aquatic epidemic for a location within the body of water is determined based on the information associated with the aquatic epidemic and water flow data associated with the body of water. An indication of the threat level is generated.

Abstract: In an approach for adaptive sampling of smart meter data, a computer retrieves one or more balancing constraints associated with one or more smart meter sensors. The computer retrieves meter sensor data from the one or more smart meter sensors according to the one or more balancing constraints. The computer determines a subsample of the meter sensor data based, at least in part, on one or more similar consumption patterns of meter sensor data, and then transmits the subsample of the meter sensor data to an optimization engine for use in solving an optimization problem.

Abstract: An apparatus sets a layer counter to point to a first layer of a data structure. The apparatus determines the layer counter to reference an overflowing cell. The apparatus increments the layer counter to point to a second layer of the data structure. The apparatus determines the incremented layer counter to reference a non-overflowing cell. The apparatus increments a value of the non-overflowing cell, wherein the first layer is stored in a first cache and the second layer is stored in a second cache, and wherein the first cache differs from the second cache with respect to one or more of speed or size.

Abstract: A method for matching first elements with second elements. Each of the first elements and second elements is a character string. The method comprises: calculating a first integer hash value for each of the first elements using a string hash function, wherein the first integer hash value is an output integer calculated from using each of the first elements as an input character string of the function; calculating second integer hash values for each of the second elements using the function; grouping each of the first elements into at least one group of a set of blocking groups using its first integer hash value; grouping each of the second elements into at the least one group of the set of blocking groups using its second integer hash value; and matching first elements with second elements within each group of the set of blocking groups using a string comparison function.